论文标题
随机电容弧路由问题
Stochastic Capacitated Arc Routing Problem
论文作者
论文摘要
本文介绍了通过在鲤鱼中的弧线上随机将数量随机数量获得的随机电容弧路由问题（SCARP）。斜坡的优化问题的特征是决策是在不知道其全部后果的情况下做出的。对于现实生活中的问题，重要的是，由于这些数量的随机性，对收集数量的变化不敏感很重要。需要有效的健壮解决方案，以避免车辆到仓位的昂贵移动。提出了不同的标准，以模拟提供遗传算法优化成本和鲁棒性的坡度和高级概念。该方法是根据Dearmon，Eglese和Belenguer提出的众所周知的实例进行了基准测试的。结果证明，可以获得强大的解决方案，而无需任何明显的溶液成本扩大。这允许处理更现实的问题，包括工业目标和与要收集数量的变化相关的约束。
This paper deals with the Stochastic Capacitated Arc Routing Problem (SCARP), obtained by randomizing quantities on the arcs in the CARP. Optimization problems for the SCARP are characterized by decisions that are made without knowing their full consequences. For real-life problems, it is important to create solutions insensitive to variations of the quantities to collect because of the randomness of these quantities. Efficient robust solutions are required to avoid unprofitable costly moves of vehicles to the depot node. Different criteria are proposed to model the SCARP and advanced concepts of a genetic algorithm optimizing both cost and robustness are provided. The method is benchmarked on the well-known instances proposed by DeArmon, Eglese and Belenguer. The results prove it is possible to obtain robust solutions without any significant enlargement of the solution cost. This allows treating more realistic problems including industrial goals and constraints linked to variations in the quantities to be collected.
